scikit-learn

E17661

Python library machine learning library open-source software

scikit-learn is a widely used open-source Python library that provides efficient tools for data mining, data analysis, and implementing a broad range of machine learning algorithms.

Try in SPARQL Jump to: Surface forms Statements Referenced by

All labels observed (10)

Label	Occurrences
scikit-learn canonical	14
FeatureUnion	1
OneHotEncoder	1
RandomForestClassifier	1
Scikit-learn	1
StandardScaler	1
scikit-learn 0.16 or earlier	1
scikit-learn 0.20	1
scikit-learn Pipeline	1
sklearn.linear_model	1

Statements (51)

Predicate	Object
instanceOf	Python library ⓘ machine learning library ⓘ open-source software ⓘ
compatibleWith	pandas ⓘ
domain	data analysis ⓘ data mining ⓘ machine learning ⓘ
hasAPI	estimator interface ⓘ
hasConcept	ColumnTransformer ⓘ scikit-learn self-linksurface differs ⓘ surface form: FeatureUnion GridSearchCV ⓘ KMeans ⓘ LogisticRegression ⓘ scikit-learn self-linksurface differs ⓘ surface form: OneHotEncoder PCA ⓘ Pipeline ⓘ scikit-learn self-linksurface differs ⓘ surface form: RandomForestClassifier RandomizedSearchCV ⓘ SVC ⓘ scikit-learn self-linksurface differs ⓘ surface form: StandardScaler fit method ⓘ fit_transform method ⓘ predict method ⓘ scorer functions ⓘ train_test_split ⓘ transform method ⓘ
license	BSD license ⓘ surface form: BSD 3-Clause License
programmingLanguage	Python ⓘ
provides	classification algorithms ⓘ clustering algorithms ⓘ dimensionality reduction methods ⓘ model selection tools ⓘ preprocessing utilities ⓘ regression algorithms ⓘ
repositoryPlatform	GitHub ⓘ
supports	cross-validation ⓘ feature extraction ⓘ feature selection ⓘ hyperparameter tuning ⓘ model evaluation ⓘ pipeline construction ⓘ semi-supervised learning ⓘ supervised learning ⓘ unsupervised learning ⓘ
targetUsers	data scientists ⓘ machine learning practitioners ⓘ researchers ⓘ
uses	NumPy ⓘ SciPy ⓘ Matplotlib ⓘ surface form: matplotlib
writtenIn	Python ⓘ

Referenced by (23)

Full triples — surface form annotated when it differs from this entity's canonical label.

Python → machineLearningLibrary → scikit-learn ⓘ

NumPy → influenced → scikit-learn ⓘ

pandas → commonlyUsedWith → scikit-learn ⓘ

scikit-learn → hasConcept → scikit-learn self-linksurface differs ⓘ

this entity surface form: FeatureUnion

scikit-learn → hasConcept → scikit-learn self-linksurface differs ⓘ

this entity surface form: StandardScaler

scikit-learn → hasConcept → scikit-learn self-linksurface differs ⓘ

this entity surface form: OneHotEncoder

scikit-learn → hasConcept → scikit-learn self-linksurface differs ⓘ

this entity surface form: RandomForestClassifier

GridSearchCV → partOf → scikit-learn ⓘ

GridSearchCV → introducedInLibrary → scikit-learn ⓘ

this entity surface form: scikit-learn 0.16 or earlier

RandomizedSearchCV → partOf → scikit-learn ⓘ

ColumnTransformer → partOf → scikit-learn ⓘ

ColumnTransformer → introducedInVersion → scikit-learn ⓘ

this entity surface form: scikit-learn 0.20

LogisticRegression → providedBy → scikit-learn ⓘ

LogisticRegression → module → scikit-learn ⓘ

this entity surface form: sklearn.linear_model

SVC → implementedInLibrary → scikit-learn ⓘ

KMeans → implementedIn → scikit-learn ⓘ

PCA → partOfLibrary → scikit-learn ⓘ

subject surface form: PCA (scikit-learn)

PCA → compatibleWith → scikit-learn ⓘ

subject surface form: PCA (scikit-learn)

this entity surface form: scikit-learn Pipeline

Vertex AI → supports → scikit-learn ⓘ

Python scientific stack → hasComponent → scikit-learn ⓘ

Azure Machine Learning → supports → scikit-learn ⓘ

NVIDIA RAPIDS → integratesWith → scikit-learn ⓘ

Amazon SageMaker → supportsFramework → scikit-learn ⓘ

this entity surface form: Scikit-learn